Coexistence-insensitive presence detection

ABSTRACT

A system and method include processing logic receiving, from a wireless transceiver of a first device, first data indicative of channel state information (CSI) of a first communication link between the wireless transceiver and a wireless transmitter of a second device, the first device and the second device being located in a building. The logic pre-preprocesses the first data to generate input vectors composed of statistical parameter values derived from sets of discrete samples of the first data. The logic processes, through a long short-term memory (LSTM) layer of a neural network, the input vectors to generate multiple hidden state values of the neural network. The logic processes, through a set of additional layers of the neural network, respective hidden state values of the multiple hidden state values to determine that a human is present in the building.

RELATED APPLICATION

This is a continuation-in-part of patent application Ser. No. 15/805,883, filed Nov. 7, 2017, which is herein incorporated by reference in its entirety.

BACKGROUND

Many buildings such as homes, retail stores, business centers, and the like, have a growing number of wireless transmission devices, including wireless transmitters and wireless receivers. These devices are sending an increasing amount of radio frequency (RF) energy through the buildings from many different directions.

BRIEF DESCRIPTION OF DRAWINGS

The present inventions will be understood more fully from the detailed description given below and from the accompanying drawings of various embodiments of the present invention, which, however, should not be taken to limit the present invention to the specific embodiments, but are for explanation and understanding only.

FIG. 1 is a block diagram of system for detecting presence of a human using an existing transmitter and one or more receivers using neural network technology, according to various embodiments.

FIG. 2A illustrates a wireless device adapted to detect and transmit channel state information (CSI) or received signal strength indicator (RSSI) data useable by a neural network (NN) to perform presence detection, according to various embodiments.

FIG. 2B is a block diagram of a detailed example of the communications interface 206 of FIG. 2A according to one embodiment.

FIG. 3 is a block diagram of architecture for employing a neural network to detect presence using channel properties data collected from a wireless device, according to various embodiments.

FIG. 4A is a flow diagram of a method for detecting presence in a building of a human using neural network processing on radio frequency (RF) channel properties received by a wireless receiver (RX), according to various embodiments.

FIG. 4B is a flow diagram of a method for learning the weights of the neural network discussed with reference to FIGS. 4A and 7A-7B, according to various embodiments.

FIG. 5A illustrates an example set of rooms in a building, each room including a particular target to be detected and an in-room receiver, according to various embodiments.

FIG. 5B illustrates the example set of rooms of FIG. 5A, but where not all of the rooms include a receiver, according to one embodiment.

FIG. 6 is a block diagram of architecture for a neural network to be employed to detect presence and location using data, including channel properties, collected from at least two transmitter and receiver pairs of wireless devices, according to various embodiments.

FIGS. 7A and 7B are a flow diagram of a method for detecting presence or location of a human in a building using neural network processing of channel properties received by a pair of receivers from at least one transmitter, according to various embodiments.

FIG. 8 is a block diagram of a user device in which embodiments of capturing CSI and RSSI for neural network processing may be implemented.

FIG. 9 is a graph illustrative of time error when labeling channel properties data according to human presence, according an embodiment.

FIG. 10 is a graph illustrative of intermittent sets of discrete samples within channel properties data that are indicative of non-motion, despite the presence of a human, according to one embodiment.

FIG. 11 is a diagram of an example of a layer portion of a long short-term memory (LSTM) neural network, according to one embodiment.

FIG. 12 is a flow chart of a method for training a sequential prediction model type of NN (e.g., LSTM NN) and for classification of channel properties data using the trained sequential prediction model type of NN, according to various embodiments.

FIG. 13 is a first block diagram of LSTM architecture for presence detection according to one embodiment.

FIG. 14 is a second block diagram of the LSTM architecture for presence detection according to one embodiment.

FIG. 15 is a graph indicative of a subcarrier index of presence detection decisions over a series of sequential time frames, according to one embodiment.

FIG. 16A is a graph indicative of channel properties data magnitude over time with a subcarrier index of 10, according to one embodiment.

FIG. 16B is a graph indicative of classification of presence of the channel properties data illustrated in the graph of FIG. 16A, according to one embodiment.

FIG. 16C is a graph indicative of classification confidence related to the classification of the presence as illustrated in the graph of FIG. 16B, according to one embodiment.

FIG. 17 is a flow diagram that illustrates preprocessing of the channel properties data before input into the sequential prediction model type of NN for training or classification, according to one embodiment.

FIG. 18 is a block diagram of classification of received channel properties data using a trained sequential prediction model type of NN (e.g., LSTM NN), according to one embodiment.

FIG. 19 is a flow diagram that illustrates compression of input vectors (containing statistical values) through processing the input vectors with a multi-layered, unsupervised learning auto-encoder (SAE) network, according to one embodiment.

FIG. 20 is a flow diagram that illustrates where the compression of the input vectors (illustrated in FIG. 19) may be sequenced within a method for classification of channel properties data to detect human presence, according to one embodiment.

FIG. 21 is a flow chart of a method for automatic labeled supervised learning using a sequential prediction model type of neural network, according to various embodiments.

FIG. 22 is a flow chart of a method for generating a detection decision using a trained sequential prediction model type of NN, according to an embodiment.

DETAILED DESCRIPTION

Until now, presence detection (e.g., detecting a human moving within or near a building or other building) has been performed through motion sensors specifically designed to detect motion of a human. Motion sensors are expensive and are additional equipment required for purchase to complete a security system. Some detection or tracking systems track passive tags carried by users or need to access built-in Global Positioning System (GPS) capability, e.g., in order to track a person's location based on a smart phone, watch, or other GPS-capable device the person is carrying. Not everyone will carry such a GPS-capable device and certainly not necessarily one that is being tracked by a security system or a home automation system, e.g., those of visitors or intruders to a home or business.

To avoid the need to deploy motion sensors, passive tags, or to rely on expensive or inaccessible GPS-capable devices (e.g., that tag specific individuals), presence detection may be performed using neural networks to analyze data received from existing wireless transmitters or wireless receivers (e.g., wireless transceivers), which are available in growing quantities in today's homes and office environments. For example, such wireless transmitters (TX) and receivers (RX) may include, but not be limited to, wireless base stations such as modems, hubs, routers, and the like, and wireless receivers, such as digital media devices, network-attached storage (NAS) devices, mobile devices, smart thermostats, and other Internet-of-things-connected devices. These wireless TX and RX devices may be referred to generally as nodes herein. A node is a device having at least an antenna, a radio, and a processing component.

More generally, wireless radio frequency (RF) signals (or simply “wireless signals”) may be employed to not only detect presence within line of sight, such as in the same room or space, but also in adjacent room(s) because wireless signals may pass through walls. These RF signals may be generated via a wireless local area network (WLAN) employing technology such as 2.4 GHz or 5.0 GHz WiFi®, Bluetooth®, ZigBee®, Zwave® and the like.

The present system and methods may detect the presence of a human in association with any of these wireless devices (e.g., when the human is within wireless range of these devices), and may do so through walls, around corners, behind doors, and through or around other objects. By applying neural network-based algorithms to data that includes radio frequency (RF) channel properties received from wireless devices, a computing device may predict the presence of a human inside of a home with a single node. The channel properties may come in different forms, such as power levels or amplitude and phase information of wireless signals in a transmission link between a wireless TX and a wireless RX. Furthermore, the computing device may achieve accurate localization of a detected human with two to three nodes, e.g., with two or more pairs of TX-RX links. The nodes may be spread out within a building and need not be placed in every room to be able to detect a human within range of a node.

For example, in one embodiment, a computing device may receive, from a wireless RX, first data indicative of channel properties of a first communication link between the wireless RX in a first device and a wireless transmitter (TX) in a second device, the first device and the second device being located in a building having one or more rooms. The computing device may process, by a neural network executed by the computing device, the first data to detect presence of a human in the building. The computing device may output result data indicative of the presence of the human. In one embodiment, the result data includes a presence indicator indicative of the presence of the human. The computing device may also transmit the presence indicator to at least one of the first device or the second device.

In various embodiments, the neural network applies a set of algorithms to the data to distinguish the human from pre-existing or stationary objects within the building. For example, the computing device may apply a separability function to the first data, to separate the channel properties according to a group of frequencies over a time period during which the wireless RX received the first data. This separation may generate a group of separated data, e.g., data associated with respective frequencies of the group of frequencies. The computing device may apply a plurality of weights to respective ones of the group of frequencies, to generate weighted data values. The computing device may combine the weighted data values to generate an output value to the neural network. The computing device may then apply an activation function to the output value to generate the presence indicator.

In an alternative embodiment, the computing device may receive data from a first wireless RX and a second wireless RX that are located in a building having one or more rooms. The data may include first channel properties in a first communication link between a wireless TX and the first wireless RX and second channel properties in a second communication link between the wireless TX and the second wireless RX. The first channel properties and the second channel properties may represent wireless signal propagation characteristics of wireless signals being transmitted within the building. The computing device may further process the first channel properties and the second channel properties with a multi-layer neural network to detect not only presence of a human in the building, but also determine a relative location of the human with respect to a closest of the first wireless RX, the second wireless RX, and the wireless TX.

In one embodiment, the computing device may generate a presence indicator (indicative of the presence) or a location indicator (indicative of the relative location), and transmit one or both of the presence indicator or the location indicator to a closest of the wireless devices in the building. In this way, the closest wireless device may become informed of, and act on, the presence of the human. For example, the closest wireless device may increase sampling of the channel properties to perform further presence detection where humans may again be expected. Other wireless devices that do not receive a presence or location indicator may reduce sampling for channel properties, and thus preserve bandwidth and reduce power consumption.

FIG. 1 is a block diagram of system 100 for detecting presence of a human using an existing transmitter (TX) 102 and one or more receiver (RX) 104A, 104B, 104C, and 104D using neural network technology, according to various embodiments. In some embodiments, a transmitter is combined within a receiver (e.g., a transceiver), which may play the role of a transmitter or receiver in different scenarios (see FIGS. 2A and 2B). The system 100 may include more or fewer of the TX 102 and the RXs 104A, 104B, 104C, and 104D, which may be located within a building, such as a home, an office space, or other meeting area.

The space depicted in FIG. 1 may, for example, be a large conference room 101 (e.g., room 101) that contains a number of wireless-capable receivers in the four corners of the room. Note that a “target route” is identified within the room as a route most likely to be traversed by people (e.g., humans) passing through the room. Also, in one example, the transmitter 102 is outside of a wall of the room 101 and therefore radiates radio frequency (RF) wireless signals through the wall. The wireless signals may be received by the receivers 104A, 104B, 104C, and 104D after any scattering (e.g., from reflection and refraction), fading, and power decay of the wireless signals (over a distance), each of which passes through various and different radio propagation paths. Accordingly, an exact location of each RX is not needed where a relative location in proximity of one of the receivers may provide a location of a human detected in the room, or detected near or in an adjacent room to a room that has a RX.

The TX 102 and RX 104A, 104B, 104C, and 104D may communicate with a computing device 150 through a network 115. The network 115 may include wireless aspects, but will likely connect wirelessly to the TX 102 and RX 104A, 104B, 104C, and 104D, e.g., by way of a local access point that provides an Internet or WAN connection. Such an Internet or WAN connection may include additional links or trunks, whether wired or wireless, that may involve other types of wideband communication, including those based on cellular standard(s).

The computing device 150 may include, for example, a processor 152 and storage device 156. The storage device 156, which may be understood to include memory or other computer-readable medium, may include neural network (NN) algorithms 158 (e.g., code for the NN algorithms), training data 160, and pre-trained classifiers 162, which may be used in performing detection and location identification of persons within buildings. The pre-trained classifiers 162 may be hundreds or even thousands of classifiers of types of objects expected to be found in rooms of the building, such as furniture, built-in buildings, plants, indoor trees, moving items (both animate and inanimate, including pets), and different sizes and shapes of humans and those humans moving in different ways. The training data 160 may later be updated over time as people come and go through the room, and the data captured at the RX 104A, 104B, 104C, and 104D include additional data, including channel properties, captured during periods of time in which the room may change, and particularly with reference to detecting people moving within the room. This updated training data may then be used to further train the pre-trained classifiers 162, so that further NN presence and location detection may be improved.

Employing trained neural networks to perform presence and location detection may be performed on different types of channel property data, including Received Signal Strength Indicator (RSSI) data, Channel State Information (CSI), or a combination of both. Additional sources of signal characteristics, power, or channel properties or other channel information may also be employed, and therefore, RSSI and CSI are but a listing of understood and available forms of channel properties.

Accordingly, in one embodiment, a wireless RX may receive and transmit RSSI, which is a parameter (e.g., channel properties) that has a value of zero (“0”) to a RSSI maximum value (referred to as “RSSI Max”), and is indicative of the signal strength of a wireless signal associated with a wireless network. Accordingly, RSSI is a measurement value of power present in received wireless signals, and contains a single value per packet. For example, RSSI can be measured as the power level of a radio frequency that a client device receives from an access point (AP), such as a wireless router. In another implementation, RSSI may be a measurement of the energy observed at the antenna by a wireless physical layer (PHY) of the receiver used to receive a current Physical Layer Convergence Protocol (PLCP) Protocol Data Unit (PPDU) of the receiver. In one implementation of a home wireless local area network (WLAN) (e.g., using the WiFi® technology), for example, the client device may generate the RSSI either based on a data frame received or a beacon from an AP node. The RSSI may fluctuate when the environment changes. Such changes can be caused by many factors, such as a moving a transmitter or receiver, moving objects nearby the AP or client, a change in ambient noise level, temperature swings, or other such factors that cause fluctuations in RSSI.

In another embodiment or implementation, a RX can measure and transmit CSI, which is data that includes channel properties of a communication link between a TX and RX. For example, a receiver within a node may retrieve the CSI from a baseband channel estimator with which to perform presence detection. The receiver may adjust the rate of sampling channel properties by the baseband channel estimator. The CSI may include a detailed channel impulse response with both amplitude and phase information across all the Orthogonal Frequency Division Multiplexing (OFDM) subcarriers and be updated (at the maximum rate) every OFDM symbol. This may provide more information about the environment under surveillance, and thus may improve detection capability when applying a NN, as discussed herein, to CSI data or CSI-liked data.

FIG. 2A illustrates a wireless device 200 adapted to detect and transmit channel state information (CSI) or received signal strength indicator (RSSI) data useable by a NN to perform presence detection, according to various embodiments. The wireless device 200 may include, but not be limited to, a transmitter (TX) 202, a receiver (RX) 204, a communications interface 206, one or more antenna 210, a memory 214, one or more input/output (I/O) devices 218, and a processor 220. In one embodiment, the transmitter 202 and the receiver 204 are combined into a transceiver. These components may all be coupled to a communications bus 230. The I/O devices 218 may include an input device such as a microphone for detecting sound, e.g., for purposes of voice recognition, and a speaker, e.g., for purposes of communicating with a person based on the voice recognition. The processor may receive audio data through the microphone and transmit audio through the speaker in response to the audio data and also in response to presence or location indicators as will be discussed

In one embodiment, the processor 220 may be configured to direct the TX 202 to transmit data, which includes channel properties, to a remote computing device over a network for neural network processing. The processor 220 may further be configured to direct the RX 204 to receive a response from the remote computing device containing a presence detection signal, which includes a neural network indicator (e.g., a presence indicator or a location indicator) that a human is present within wireless range of the antenna 210. The location indicator may make reference to a wireless RX that was the closest to detect the human, and in one example, is the wireless RX 204 of the wireless device 200.

The antennas (such as the antenna 210) described herein within various devices may be used for Long Term Evolution (LTE) frequency bands, third generation (3G) frequency bands, Wi-Fi® and Bluetooth® frequency bands or other wireless local area network (WLAN) frequency bands, including Zigbee®, Z-wave™ or the like, wide area network (WAN) frequency bands, global navigation satellite system (GNSS) frequency bands such as global positioning system (GPS) frequency bands, or the like.

FIG. 2B is a block diagram of a detailed example of the communications interface 206 of FIG. 2A. As illustrated in FIG. 2B, the communications interface 206 may further include RF circuitry 283, a set of RF modules 286, and a baseband channel estimator 290. A more detailed and alternative embodiment of a wireless device will be discussed with reference to FIG. 8. In one embodiment, one of the RF modules 286 may include a WiFi® PHY at which the RF energy of received RF signals may be measured for purposes of RSSI. The baseband channel estimator 290, by virtue of being incorporated within the communications interface 206, may be coupled to the antenna 210, the TX 202, and to the RX 204, and be adapted to estimate the CSI (or the RSSI) for each channel. As discussed, the CSI includes a detailed channel impulse response (e.g., containing channel properties) with both amplitude and phase information across all the OFDM subcarriers and is updated (at the maximum rate) every OFDM symbol. This provides more information about the environment under surveillance, and thus provides excellent detection capability when applying a NN, as discussed herein, to CSI data.

The wireless device 200 may, in various embodiments, continuously upload RSSI or CSI data to the computing device 150 (FIG. 1) for use in NN processing, or may buffer the RSSI or CSI data in the memory 214 (or other computer storage) and then periodically upload the RSSI or CSI data at a predetermined time interval. In one embodiment, the wireless device 200 (or a co-located computing system) is adapted to include sufficient memory, storage, and processor power to be able to directly perform the NN processing. Where these lack, however, the data upload to the computing device 150 may allow the NN processing to be performed in the cloud, e.g., on a networked server that receives the CSI or RSSI data from the wireless device 200 and, in some examples, from multiple wireless devices 200 for a given building.

In other embodiments, the wireless device 200 (or co-located computing system) may perform a portion of the NN processing. For example, the wireless device 200 may store in the memory 214 (or other computer storage) a copy of one or more classifiers received from a remote computing device such as the networked server. A classifier may have been trained to detect a human with the building, and thus is already adapted to the environment of the building. The wireless device 200 may then process updates to data containing channel properties, to generate updated data that contains recently received channel properties. This updated data may then be processed, by applying multiple weights from the classifier and within a NN, to generate a prediction output indicative of the presence of the human. The use of the multiple weights within the NN will be discussed in more detail.

In various embodiments, the wireless device 200 may perform selective sampling of certain targeted routes or areas within a building where people have traversed multiple times in the past, and thus reduce the amount of unimportant data collected. To do so, the wireless device 200 (or a cloud server device) may monitor human traffic from past presence and location indicators (from NN processing) and identify a particular route based on presence patterns detected with the channel properties over time. For example, the origin of reflected signals may be detected as beginning in a first room, terminating in a third room, and passing through a second room, which rooms define the particular route. In one embodiment, data received and processed by the cloud server device may identify a presence pattern and transmit the presence pattern back to the wireless device 200, which may then determine that it is within wireless range of one or more portions of the route defined by the presence pattern. Furthermore, based on the prior knowledge of presence, locations, and walking routes of people in the building, the wireless devices nearest the particular routes that have been identified may actively monitor the channel properties and conduct at least a portion of the NN algorithm to determine presence and optionally location. To actively monitor may mean to increase a level of sampling of channel properties data by the baseband channel estimator 290 compared to a previous, periodic mode of sampling.

Additionally, the baseband channel estimator 290 of wireless devices in idle regions (e.g., parts of the building where humans rarely if ever walk) may slowly sample the channel properties until the channel properties are detected. To slowly sample channel properties means to sample at a rate slower than a previous, periodic mode of sampling. Avoiding a higher sampling rate means that wireless devices that are not actively monitoring for channel properties may reduce power consumption and traffic through its channels, thus also improving bandwidth.

In this way, the data about the unimportant communication link/channel properties will be reduced so that the power consumption of RF devices is also reduced. If performed by a remote server, the location of the route may be communicated back to the wireless device 200. With this information, the wireless device 200 may focus selective sampling in a pattern on that particular route and optionally throughout the day on a schedule depending on day and time, and what are the usual patterns for the people living in a particular building. For example, the processor 220 may direct the baseband channel estimator 206 to frequently sample the channel properties associated with a particular, previously dignified route, to save on bandwidth, processing power, and required storage in collecting relevant channel properties within CSI or RSSI data.

With further reference to FIGS. 1 and 2A, the computing device 150 may execute one or more algorithms to train the NN, using either supervised or unsupervised learning, to learn best how to distinguish human occupants from other objects normally expected in the building or in a building of that type. With supervised learning, NN-based algorithms are provided output datasets (e.g., the training data 160), which have been pre-classified (or pre-labeled) as associated with certain objects. The supervised learning attempts to map the data onto the pre-classified objects. Supervised learning uses a set of example pairs (x, y), x∈X, y∈Y and the aim is to find a function, f:X→Y, in the allowed class of functions that matches the example objects. In other words, the computing device executing the supervised learning seeks to infer the mapping implied by the RF characteristic measurement data. The computing device may also employ a cost function that is related to a mismatch between the mapping and the CSI data and that implicitly contains prior knowledge about the problem domain, e.g., various buildings in which detection is desired.

One example of a cost function is the mean-squared error. In another embodiment, other cost functions may be used. For example, if the softmax classifier is used (where the output are finite classes (e.g. either 1 or 0), the logarithmic cost function performs better than mean-square cost function, as the mean-squared error cost function is not always convex function in all optimization problems. For example, the logarithmic cost function may be expressed as C=−(1−y) log(1−f(x))+y log(f(x)). The mean-squared error cost function attempts to minimize the average squared error between the network's output, f(x), and the target value y over all the example pairs. Minimizing this cost using gradient descent for the class of neural networks is called multilayer perceptrons (MLP), which produces the backpropagation algorithm for training neural networks. Backpropagation is a method to calculate the gradient of the loss function (produces the cost associated with a given state) with respect to the weights in the NN. A perceptron algorithm is an algorithm for supervised learning of binary classifiers, e.g., functions that can decide whether an input, represented by a vector of numbers (e.g., frequencies in this case), belong to some specific class or not. Accordingly, the computing device 150 may apply a perceptron algorithm to the training data 160 to train the NN 158 and create additional pre-trained classifiers, and the training may be updated periodically based on updates to the training data 160 over time.

Tasks that fall within the paradigm of supervised learning are pattern recognition (also known as classification) and regression (also known as function approximation). The supervised learning paradigm is also applicable to sequential data (e.g., for presence detection, shape, and movement recognition). This can be thought of as learning with a “teacher,” in the form of a function that provides continuous feedback on the quality of solutions obtained thus far.

With unsupervised learning, no datasets are provided; instead, the received data is clustered into different classes, which may then be categorized as certain objects. In unsupervised learning, some data x is given as is the cost function to be minimized, which may be a function of the data x and the network's output, f. The cost function is dependent on the task (the model domain, such as presence or location detection) and any a priori assumptions (the implicit properties of the model, its parameters, and the observed variables).

As just one example of unsupervised learning, consider the model given by f(x)=a where a is a constant and the cost is given as C=E [(y−f(x))²]. Minimizing this cost produces a value of a that is equal to the mean of the data. The cost function can be much more complicated, of course. The form of the cost function depends on the application. For example, in compression, the form could be related to the mutual information between y and f(x), whereas in statistical modeling, the form could be related to the posterior probability of the model given the data. Tasks that fall within the paradigm of unsupervised learning are in general estimation problems; the applications include clustering, the estimation of statistical distributions, compression, and filtering. While detection in the present embodiments may be performed using both kinds of learning, supervised learning may be performed much quicker than unsupervised learning in terms of real-time performance after classifiers have been pre-trained.

In one embodiment, if training a neural network using RSSI data as the training data 160, the major part of a machine learning algorithm may need to be to be trained and refined at a customer's premises (e.g., using a network-connected device located at the premises of the room 100). Indeed, RSSI with maximum likelihood may not be sufficient for presence detection as there is no method for generating assets of known data for the on-premises training. In at least one embodiment, use of a wireless device (such as the wireless device 200 having a microphone and speaker) that is capable of voice detection may be able to tag RSSI data with human presence, thus enabling the on-premises training to take place. For example, when a user utters a wake-up word, the wireless device 200 may tag the RSSI from the built-in WiFi® of the RF modules 286, and use that as the training data for the maximum likelihood for the RSSI.

FIG. 3 is a block diagram of architecture 300 for employing a neural network to detect presence using channel properties data (e.g., RSSI, CSI) collected from a wireless device, according to various embodiments. The architecture 300 for one of the disclosed neural networks (NN) 158 may include an input layer 310, a hidden layer 320, and an output layer 330. The input layer 310 may apply, for example, a separability function to collected data to separate channel properties according to multiple frequencies over a time period during which the channel properties were received. More specifically, the separability function may separate the channel properties associated with a first group of frequencies from channel properties associated with a second group of frequencies over a time period, and so on through channel properties received from additional nodes, to generate a set of input variables to the NN. The multiple frequencies may be arrayed within a vector, {right arrow over (f)}, and associated with a corresponding time vector, {right arrow over (t)}, for purposes of algorithmic processing, as will be discussed in more detail.

In various embodiments, the hidden layer 320 may apply weights (w_(i)), which have been previously learned during NN training, to respective input variables of the set of input variables. The application of these weights (w_(i)) may weigh separated channel properties embodied within the input variables being processed by the NN 300, and thus generate weighted data values associated with the channel properties corresponding to the multiple frequencies.

The output layer 330, in one embodiment, may calculate a weighted sum 332 by combining the weighted data values from the hidden layer 320, and optionally applying any bias (θ) 336 or offset, which is dependent on the training dataset (i.e. the channel environment). The weights (w_(i)) may be learned using a learning algorithm (e.g., machine learning), whether supervised or unsupervised. The output layer 330 may complete the detection by applying a detection activation function 334 to the weighted sum, which as illustrated may be a step-wise function, such that an output value from the activation function being greater than zero results in presence detection. In computational networks, the activation function of a node defines the output of that node given an input or set of inputs. The continuous real numbered input set is transferred into binary valued output 0 or 1, which denotes the binary classes (e.g. the presence or idleness of a room)

With additional reference to FIG. 3, the computing device 150 (FIG. 1) may employ a NN set of algorithms to detect passive human presence with a wireless TX and a wireless RX, e.g., with one TX-RX link. In one embodiment, the computing device 150 utilizes the channel properties data provided by the receiver's wireless module, e.g., of the RF modules 286 (FIG. 2B). Given CSI as merely exemplary, wireless devices may acquire either the instantaneous CSI or its second order statistics of CSI during their regular operation, e.g., statistical channel properties. In a short time, there are many samples of instantaneous CSIs (i.e. instantaneous channel properties). When the room is idle, the channel properties are stationary so the variation of instantaneous CSIs is small. However, if the human is present in the room, the wireless propagation channel may be disturbed by the human behavior that is varying in a short time. For this reason, the variation of sampled instantaneous CSIs is varying quickly in a short time. Based on this underlying principle, the covariance of sampled CSI of multiple frequencies over a time period is large in the presence of human and small in an idle room. Accordingly, statistical channel properties may be the covariance of channel properties over a time period.

In one embodiment, if only the statistical CSI is available, then the input layer 310 of the neural network may be defined as: {right arrow over (x)}=Vec

(Cov

(H({right arrow over (f)},{right arrow over (t)})

  (1) where H({right arrow over (f)}, {right arrow over (t)}) is the CSI acquired for the group of frequencies within vector {right arrow over (f)} over a time period within vector {right arrow over (t)}, the Cov( ) function estimates the covariance, and Vec( ) performs a column stacking function to vectorize the covariance to contain the channel properties data associated with the group of frequencies, thus generating a group of separated data for NN processing. A column stacking function may be performed on a series of matrices, each matrix containing statistical channel properties data, to generate an input vector to the NN. If, however, the instantaneous CSI is also available, then the input layer may be given by {right arrow over (x)}=ϕ

H({right arrow over (f)},{right arrow over (t)}

  (2) where the ϕ( ) is a separability function selected to optimize data separability, e.g., separate channel properties data according to multiple distinct frequencies located in the frequency vector, {right arrow over (f)}.

For both cases, the output layer may be calculated as

$\begin{matrix} {y = {{\sum\limits_{i = 1}^{N}{w_{i}x_{i}}} + \theta}} & (3) \end{matrix}$ where N is the number nodes in the hidden layer 320 for which the weighted sum 332 is applied, θ is the bias term, and the w_(i) are the weights applied within the hidden layer(s) 320 that were previously trained using the training data 160. Using supervised learning, the computing device 150 may apply a perceptron algorithm to the training data 160 according to pre-trained classifiers 162. In one embodiment, the computing device 150 may further update the training of the pre-trained classifiers 162 based on updated training data from channel properties received from the RX and possibly also the TX.

At the output layer 330, the computing device executing the NN may determine that the null hypothesis of the target is present in the area of interest via the activation function 334 (e.g., a step function) of H ₀=sign(y),  (4) where H₀=1 indicates a target is present, e.g., a human person of interest. Accordingly, a value of H₀ over zero may be indicative of human presence. The computing device may then generate a detection indicator that may be sent to either of the RX or TX wireless devices.

In an alternative embodiment, when employing unsupervised learning as a basis for training the NN simultaneously with applying the NN to channel properties data, the computing device 150 may perform expectation maximization for a multivariate Gaussian mixture model given by ϕ={(G _(i)),μ_(i),Σ_(i)}_(i=0) ^(k)  (5) where P(G_(i)) is the probability of mixture, i, with the distribution P(X|G_(i))˜N(μ_(i),Σ_(i)), where i=0 indicates an idle room and i=1, . . . , k indicates when the target (e.g., target person) is in room, i. Furthermore, the expression L_(C)(ϕ|X, Z) is the complete likelihood of the model ϕ with respect to observed variables X and hidden variables Z.

The unsupervised learning may be performed for the expectation maximization algorithm by estimating a plurality of weights of model states via initial guesses for the parameters {P(G_(i)),μ_(i),Σ_(i)}. After estimating these parameters, the computing device 150 may perform an expectation step given by Q(ϕ|ϕ^(l))=E[L _(C)(ϕ|X,Z),ϕ^(l)].  (6) This expectation step may be followed by a maximization step given by:

$\begin{matrix} {\phi^{l + 1} = {\underset{\phi}{argmax}{{Q\left( \phi \middle| \phi^{l} \right)}.}}} & (7) \end{matrix}$ The decision maximum likelihood may be expressed as x_(t)∈G_(n) if argmax_(i)P(G_(i)|x_(t))=n.

FIG. 4A is a flow diagram of a method 400 for detecting presence in a building of a human using neural network processing on RF channel properties received by a wireless receiver (RX), according to various embodiments. Method 400 may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (such as instructions running on the processor), firmware or a combination thereof. In one embodiment, a processor of the computing device 150 or the wireless device 200 performs the method 400. Alternatively, other components of the user device may perform some or all of the operations of the method 400.

The method 400 may begin with processing logic receiving, from a wireless RX, first data indicative of channel properties of a first communication link between the wireless RX in a first device and a wireless transmitter (TX) in a second device, the first device and the second device being located in a building having one or more rooms (410). The method 400 may continue with processing, by a neural network executed by the processing logic, the first data to detect presence of a human in the building (415). The method 400 may continue with the processing logic outputting result data indicative of the presence of the human within the building (435). The method 400 may continue with the processing logic generating and transmitting a presence indicator, indicative of the presence of the human, to at least one of the first device or the second device (440). In one embodiment, the result data includes the presence indicator, such that the first device and/or second device are informed of the presence.

In various embodiments, the neural network applies a set of algorithms to the data to distinguish the human from pre-existing or stationary objects within the building. In one embodiment, the method 400 continues from block 415 with the processing logic applying a separability function to the first data, to separate the channel properties according to a group of frequencies that carried the first data over a time period during which the wireless RX received the first data, thus generating a set of input variables (420). The method 400 continues with the processing logic applying a plurality of weights to respective ones of the set of input variables, to generate weighted data values (425). The method 400 may continue with the processing logic combining the weighted data values to generate an output value (430). The method 400 may continue with the processing logic applying a detection activation function to the output value to generate the presence indicator (435). In one embodiment, the detection activation function comprises a step function, which activates the presence indicator for values output from the activation function that are greater than zero.

FIG. 4B is a flow diagram of a method 450 for learning the weights w_(i) of the neural network discussed with reference to FIG. 4A and FIGS. 7A-7B with a perceptron algorithm, according to various embodiments. A perceptron algorithm with reference to artificial neural networks is an algorithm used for pattern recognition, e.g., in an effort to properly classify new training data as either an object in a building or a human present within the building. Method 450 may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (such as instructions running on the processor), firmware or a combination thereof. In one embodiment, a processor of the computing device 150 or the wireless device 200 performs the method 450. Alternatively, other components of the user device may perform some or all of the operations of the method 450.

In various embodiments, the weights (w_(i)) are learned from the labeled training data with the perceptron algorithm and in relation to the architecture 300 of the neural network. With reference to FIG. 4B, the method may begin with the processing logic initializing w_(i)'s randomly, e.g., randomly selecting starting values for each w_(i) (455). The method 450 may continue with the processing logic selecting a sample, x(n), from the training data received from the receiver (460). The method 450 may continue with the processing logic classifying the x(n) sample with the current weights w_(i) (465). The method 450 may continue with the processing logic determining whether the classification of the x(n) is correct, e.g., by way of comparison with predetermined labels of labeled training data (470). If the classification is correct, the method 450 may continue with the processing logic terminating training for the received training data (475).

With continued reference to FIG. 4, if the classification is not correct as determined at block 470, the method 450 may continue with the processing logic updating w (e.g., the current weight w_(i) for the sample x(n)) to be equal to w _(i) +ηd(n)x _(i)(n)∀i.  (8) where d(n)∈{−1,1} is the output range (480). The method 450 may continue with the processing logic determining whether there is still unclassified training data remaining (485). If not, the method 450 may terminate; otherwise, the method 450 may continue with the processing logic looping back to selecting a new sample x(n) of CSI from the training data received from the receiver (460), to continue with training the NN with the training data. As discussed with reference to FIG. 3, these trained weights (w_(i)) may then be used in combination with the CSI data and any bias term to generate an output as a weighted sum, which may then be passed through a detection activation function to determine whether or not a presence is detected.

In various embodiments, once presence is detected, the computing device 150 may send a presence detection signal to one or both of the RX and TX (e.g., the wireless device 200) in the premises of the monitored building. Furthermore, the wireless device 200 that receives the presence detection signal may perform a predetermined or programmed action. For example, if associated with a home security or automation system, the wireless device 200 may sound an alarm or send an intrusion detection signal to law enforcement if the presence is unanticipated. If the presence is anticipated, the wireless device 200 may start streaming a certain kind of music or perform other follow-me services in conjunction with Internet-of-things devices. For example, the follow-me services may include turning on lights in rooms where the person goes, starting a program on a television (e.g., the next of the series on a streaming service) in that room, passing the music into additional room, or starting a pot of coffee or the like for the person.

By way of experiment with reference to presence detection within the room 101 shown in FIG. 1, the results in the Table 1 illustrate the effectiveness, on a RX location basis, of the neural network (NN) method (integrated with CSI) compared to use of just CSI and to other RSSI-related methods, which include RSSI, differential RSSI, and entropy. In this experiment, the room 101 measured length (D₁) as 30 feet and width (D₂) as 14 feet.

TABLE 1 NN RS SI diff-RSSI Entropy CSI RX #1  99% 88% 99% 77%  99% RX #2  99% 90% 96% 68%  98% RX #3  97% 59% 56% 13%  98% RX #4 100% 95% 94% 82% 100%

FIG. 5A illustrates an example set of rooms in a building 500, each room including a particular target to be detected, according to various embodiments. The rooms may be identified herein according to receiver (RX) or target number. In this experiment, the target to be detected is not a human person but a predetermined object located at differing locations with reference to the receiver that was in a correspondingly-numbered room. This provides an indication of differing levels of effectiveness depending on mutual locations and what type of method was used for detection, including the detection of targets through walls in different parts of the building 500. Note that although rooms are referred to, a building may of course have a relatively open constructions style with few walls, and thus few “rooms.” Accordingly, the term “rooms” may also be understood to refer to portions or areas within a building, where receivers and transmitters may be distributed throughout an open space.

As can be seen in Table 2, the decision based on RSSI requires at least one node in each room, CSI and NN perform better for the cases when either a transmitter or a receiver is in the same room with the target, and NN learning with CSI overall has the best coverage. In this particular experiment, the building measured length (D₁) as 71 feet and width (D₂) as 40 feet, including four different rooms, some inner stairwell space, and other closet space.

TABLE 2 RX # Target # NN RSSI diff-RSSI Entropy CSI 1 1 93.5% 81.5% 90.5% 92.0% 93.5% 1 2 74.5% 62.5% 67.5% 71.5% 77.0% 1 3 66.0% 50.5% 50.0% 56.5% 55.0% 1 4 73.5% 51.5% 50.0% 51.5% 52.0% 2 1 80.5% 69.5% 69.0% 51.5% 83.5% 2 2 93.0% 80.0% 83.0% 69.5% 93.5% 2 3 79.0% 50.5% 50.5% 31.0% 56.5% 2 4 93.0% 51.5% 50.0% 48.5% 47.5% 3 1 53.5% 50.5% 50.5% 56.0% 54.5% 3 2 72.0% 50.5% 51.5% 57.0% 55.0% 3 3 98.0% 73.0% 84.5% 93.5% 95.5% 3 4 71.5% 52.5% 52.0% 61.5% 61.0% 4 1 62.0% 50.0% 50.0% 43.0% 54.5% 4 2 71.0% 50.5% 50.0% 39.5% 59.0% 4 3 68.0% 67.0% 72.0% 68.5% 73.5% 4 4 94.0% 84.5% 94.0% 85.5% 90.0%

With continued reference to FIG. 5A, Table 3 illustrates a closer inspection of results with use of a neural network (NN) approach using CSI-based data. Table 3 illustrates, along the diagonal (in bold), the NN detection results when the RX and the target are in the same room. Note that the NN performs better than 93% if the target and the RX are in the same room. The results illustrated in Table 3 that are off the diagonal illustrate a percentage of detection success when the RX was in a different room from the detected target. Even when the target and the RX are at opposite sides of the building 500, the NN can still achieve more than 70% accuracy (e.g., RX1 detecting target 4 at 73.5% of the time). By utilizing multiple TX-RX links simultaneously (e.g., together during the same training), one can significantly improve the detection rate throughout a home or other building.

TABLE 3 Target RX 1 2 3 4 1 93.5% 74.5% 66.0% 73.5% 2 80.5% 93.0% 79.0% 93.0% 3 53.5% 72.0% 98.0% 71.5% 4   62%   71%   68%   94%

FIG. 5B illustrates the example set of rooms of FIG. 5A, but where not all of the rooms include a receiver. Note that even though there is no longer a receiver within the same room with Target #3, that the other receivers (RX #1, #2, and #3) may receive data used to detect presence of Target #3. These other receivers (RX #1, #2, and #3) may receive data to detect Target #3, where the data includes channel properties received related to wireless signals that have passed through the walls and passages of the building and reflected and refracted into these other receivers (RX #1, #2, and #3). Accordingly, the presence may still be detected if a human is within wireless range of any receiver, and may be associated with the closest of the receivers (RX #1, #2, and #3). This means that not only does a human not need to be actively tagged to detect that person, but the disclosed embodiments may detect the presence of that human without even having a receiver or other sensor in the same room, although as will be discussed, some sensor may aid in initial training of a neural network. This is a distinct advantage over current systems that require motion sensors or the like that only work within the room or space in which they are placed due to reliance on line of sight for detection.

FIG. 6 is a block diagram of architecture 600 for a neural network to be employed to detect presence and location using data, including channel properties, collected from at least two transmitter and receiver pairs of wireless devices, according to various embodiments. If the channel properties from more than one wireless TX-RX pairs are available, then a multi-layer NN can be used not only to detect passive human presence, but also to determine the relative location of the target with respect to the wireless nodes (whether TX or RX nodes). For example, the computing device 150 may receive, from a first wireless RX and a second wireless RX that are located in a building, data that includes first channel properties in a first communication link between a wireless TX and the first wireless RX and second channel properties in a second communication link between the wireless TX and the second wireless RX. As discussed, the channel properties may represent wireless signal propagation characteristics of wireless signals being transmitted within the building.

The architecture 600 of FIG. 6 extends on the architecture 300 (FIG. 3) and similarly includes an input layer 610, one or more hidden layers 620, and an output layer 630. The input layer 610 of the NN architecture 600 of FIG. 6 may be expressed as: {right arrow over (x)}=ϕ(H ₁({right arrow over (f)},{right arrow over (t)}),H ₂({right arrow over (f)},{right arrow over (t)}), . . . ,H _(K)({right arrow over (f)},{right arrow over (t)})  (9) where K is the number of TX-RX links, and thus acts as a separability function applied to at least the first channel properties and the second channel properties within nodes of the input layer 610. The separability function may separate the channel properties associated a first group of frequencies from channel properties associated with a second group of frequencies over the time period, and so on through channel properties received from additional nodes, to generate input variables to the NN. As discussed, these groups of frequencies may be carried with a vector of the frequencies, {right arrow over (f)}, and associated with a corresponding vector of time, {right arrow over (t)}, over the time period, for purposes of NN processing. Multiple hidden layers 620 may be employed to process the input variables from the input layer 610. For example, a first hidden layer may apply multiple weights to respective input variables to calculate a summed output:

$\begin{matrix} {{y_{1,k} = {{\sum\limits_{i = 1}^{N_{hidden}}{w_{1,k,i}x_{1,i}}} + {\theta_{k}\mspace{14mu}{\forall{{k\; 0} < k \leq K}}}}},} & (10) \end{matrix}$ where y_(1,k) is a summation of multiple weighted variables, one for each of the K nodes, for a given hidden layer 620. So, a first weighted variable (from a weighted first input variable) may be added to a second weighted variable (from a weighted second input variable) to generate the summed output.

Accordingly, the multiple summers 631A, 632B . . . 632 n may represent integration of various trained weights for each TX-RX link, generating the y_(1,k) summed output. In one embodiment, the weights (w_(1,k,i) w_(2,n,i)) may have been previously trained through application of a perceptron algorithm with back-propagation and in view of pre-trained classifiers, which may training or labeled data used for training. The training may be performed with the computing device 150 executing supervised learning, which methods were discussed in detail previously. In one embodiment, the supervised learning is based on data captured from the building indicative of an initial state of a topology of the building without humans.

Then, by applying a sigmoid activation function 634A, 634B . . . 634 n to the summed output, y_(1,k), the input of a second of the hidden layers 620 may be determined as:

$\begin{matrix} {{x_{2,k} = {\frac{1}{1 + e^{- y_{1,k}}}{\forall{{k\; 0} < k \leq K}}}},} & (11) \end{matrix}$ which may also be referred to as intermediate output value(s) that becomes a new input variable to a second hidden layer within the hidden layers 620. The sigmoid activation function may map all possible y_(1,k) values to the interval of [0,1], which can also be interpreted as the likelihood of the target being close to a kth node. The sigmoid activation function works well in a NN in taking, as an input values, any real numbers, and returning an output value that monotonically increases between two values, for example, between zero (“0”) and (“1”). The second hidden layer may then process the intermediate output value(s) output by the sigmoid activation function using Equation (10), to generate further intermediate output values.

The computing device 150 may then combine intermediate output value(s) from the hidden layers 620 using multiple summers 624A, 624B, to generate a final output value for deciding whether presence has been detected, and if so, a possible location of a human target. More specially, an algorithm in the output layer 630 for determining a final output value may be expressed as:

$\begin{matrix} {y_{2,k} = {{\sum\limits_{i = 1}^{K}{w_{2,n,i}x_{2,i}}} + {\theta\mspace{20mu}{\forall{{k\; 0} < n \leq {N_{outputs}.}}}}}} & (12) \end{matrix}$ In other words, this final output value is the summation of a combination of an additional multiple weights, w_(2,n,i,) one for each node, and corresponding intermediate output value(s), x_(2,i), along with a possible biasing term, θ, as expressed in Equation (12).

The NN may then determine the null hypothesis of the target is present in the area of interest via a detection activation function 664 (e.g., a step function) applied to the final output value, which may be expressed as: H ₀=sign(y _(2,1)),  (13) where H₀=1 indicates a target (e.g., a human) is present. Other detection activation functions may also be employed. In addition, the relative location of the target may be determined by a locator function 666 applied to the final output value, which may be expressed as: loc_(id)=ψ(y _(2,2))  (14) where ψ( ) is a function trained or derived from labeled training data. The locator function 666 may generate a location indicator of a location of a human detected within the building. The locator function is a logistic transfer function which outputs finite classes (e.g., room number in a house or the partition of a building) depending on the input vector, which is the output of the hidden layer nodes 634A-634N.

Accordingly, the location indicator is associated with a closest wireless RX or wireless TX to the human (e.g., take on a name associated with the room in which the closest wireless RX or wireless TX is located and which devices are similarly named). This location indicator may be transmitted to the closest wireless RX or wireless TX so that the closest wireless RX or wireless TX may take predetermined action depending on programming and context provided with the presence detection and the location determination. These possible applications were discussed previously such performing security-related, home-automation-related functions, or Internet-of-things (IOT)-related in response to receipt of the location indicator. In various embodiments, the weights w_(1,k,i) w_(2,n,i) and the function ψ( ) may be obtained with a perceptron learning algorithm with back-propagation in view of pre-trained classifiers.

FIGS. 7A and 7B are a flow diagram of a method 700 for detecting presence or location of a human in a building using neural network (NN) processing of channel properties received by a pair of receivers from at least one transmitter, according to various embodiments. Method 700 may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (such as instructions running on the processor), firmware or a combination thereof. In one embodiment, a processor of the computing device 150 or the wireless device 200 performs the method 700. Alternatively, other components of the user device may perform some or all of the operations of the method 700.

With further reference to FIG. 7A, the method 700 may begin with the processing logic receiving, from a first wireless receiver (RX) located in a building, data that includes first channel properties in a first communication link between a wireless transmitter (TX) and the first wireless RX that existed during a time period (710). The method 700 may continue with the processing logic receiving second channel properties in a second communication link between the wireless TX and the second wireless RX that existed during the time period (715). In one embodiment, channel properties represent wireless signal propagation characteristics of wireless signals being transmitted within the building. The method 700 may continue with the processing logic applying, in an input layer of a neural network, a separability function to the data to separate the first channel properties associated with a first group of frequencies from the second channel properties associated with a second group of frequencies, to generate a first input variable and a second input variable, respectively, to a multi-layered neural network (720). In one embodiment, the separability function is selected to optimize data separability of the first channel properties from the second channel properties, and according to the groups of frequencies for each of the first channel properties and the second channel properties.

The method 700 may continue with the processing logic, within a first hidden layer, applying a first weight of a plurality of weights to the first input variable, to generate a first weighted variable (730A), and applying a second weight of the plurality of weights to the second input variable, to generate a second weighted variable (730B). The method 700 may continue with the processing logic summing the first weighted variable and the second weighted variable to generate a first summed output (740). This output is referred to as a summed output by virtue of application of Equation (10), where the weighted frequencies are summed. The method 700 may continue with the processing logic calculating, a first intermediate output value by applying a sigmoid activation function to the first summed output (740). In one embodiment, the sigmoid activation function maps the first summed output to an interval between zero and one. The method 700 may continue with the processing logic applying a third weight (or a first weight of a second plurality of weights) to the intermediate output value, to generate a second intermediate output value (745). The second intermediate output value may be generated in conjunction with a second hidden layer, and additional intermediate outputs may be generated for more nodes (e.g., RX-TX pairs) and for additional hidden layers of the multi-layered NN.

With further reference to FIGS. 6 and 7B, the method 700 may continue, within an output layer of the NN, with the processing logic summing the first intermediate output value, the second intermediate output value, and any additional intermediate output values (from further RX-TX pairs and across one or more additional hidden layers), to generate a final output value (750). The method 700 may continue with the processing logic performing at least one of the following, e.g., applying a detection activation function (e.g., a step function) to the final output value to detect presence of a human within the building (760A) and applying a locator function to the final output value to detect a location of the human within the building (760B). The location may include a location indicator associated with a closest of the first wireless RX and the second wireless RX to the location. The method 700 may continue with the processing logic transmitting a presence indicator (from block 760A) or the location indicator (form block 760B) to at least one of the first wireless RX, the second wireless RX, or the wireless TX (770).

FIG. 8 is a block diagram of a user device 805 in which embodiments of capturing CSI and RSSI for neural network processing may be implemented. The user device 805 may correspond to the RX wireless devices 104A, 104B, 104C, 104D or TX wireless device 102 of FIG. 1, the computing device 150 of FIG. 1, or the wireless device 200 of FIGS. 2A and 2B. The user device 805 may be any type of computing device such as an electronic book reader, a PDA, a mobile phone, a laptop computer, a portable media player, a tablet computer, a camera, a video camera, a netbook, a desktop computer, a gaming console, a DVD player, a computing pad, a media center, and the like. The user device 805 may be any portable or stationary user device. For example, the user device 805 may be an intelligent voice control and speaker system. Alternatively, the user device 805 can be any other device used in a WLAN network (e.g., Wi-Fi® network), a WAN network, or the like.

The user device 805 includes one or more processor(s) 830, such as one or more CPUs, microcontrollers, field programmable gate arrays, or other types of processors. The user device 805 also includes system memory 806, which may correspond to any combination of volatile and/or non-volatile storage mechanisms. The system memory 806 stores information that provides operating system component 808, various program modules 810, program data 812, and/or other components. In one embodiment, the system memory 806 stores instructions of the methods 400, 440, and 700 as described herein. The user device 805 performs functions by using the processor(s) 830 to execute instructions provided by the system memory 806.

The user device 805 also includes a data storage device 814 that may be composed of one or more types of removable storage and/or one or more types of non-removable storage. The data storage device 814 includes a non-transitory computer-readable storage medium 816 on which is stored one or more sets of instructions embodying any of the methodologies or functions described herein. Instructions for the program modules 810 may reside, completely or at least partially, within the computer-readable storage medium 816, system memory 806 and/or within the processor(s) 830 during execution thereof by the user device 805, the system memory 806 and the processor(s) 830 also constituting computer-readable media. The user device 805 may also include one or more input devices 818 (keyboard, mouse device, specialized selection keys, etc.) and one or more output devices 820 (displays, printers, audio output mechanisms, etc.).

The user device 805 further includes a modem 822 to allow the user device 805 to communicate via a wireless network (e.g., such as provided by the wireless communication system) with other computing devices, such as remote computers, an item providing system, and so forth. The modem 822 can be connected to RF circuitry 883 and zero or more RF modules 886. The RF circuitry 883 may be a WLAN module, a WAN module, PAN module, or the like. Antennas 888 are coupled to the RF circuitry 883, which is coupled to the modem 822. Zero or more antennas 884 can be coupled to one or more RF modules 886, which are also connected to the modem 822. The zero or more antennas 884 may be GPS antennas, NFC antennas, other WAN antennas, WLAN or PAN antennas, or the like. The modem 822 allows the user device 805 to handle both voice and non-voice communications (such as communications for text messages, multimedia messages, media downloads, web browsing, etc.) with a wireless communication system. The modem 822 may provide network connectivity using various types of mobile network technology including, for example, cellular digital packet data (CDPD), general packet radio service (GPRS), EDGE, universal mobile telecommunications system (UMTS), 1 times radio transmission technology (1×RTT), evaluation data optimized (EVDO), high-speed down-link packet access (HSDPA), Wi-Fi®, Long Term Evolution (LTE) and LTE Advanced (sometimes generally referred to as 4G), etc., although not all of these mobile network technologies may be available.

The modem 822 may generate signals and send these signals to antenna 888, and 884 via RF circuitry 883, and RF module(s) 886 as descried herein. User device 805 may additionally include a WLAN module, a GPS receiver, a PAN transceiver and/or other RF modules. These RF modules may additionally or alternatively be connected to one or more of antennas 884, 888. Antennas 884, 888 may be configured to transmit in different frequency bands and/or using different wireless communication protocols. The antennas 884, 888 may be directional, omnidirectional, or non-directional antennas. In addition to sending data, antennas 884, 888 may also receive data, which is sent to appropriate RF modules connected to the antennas.

In one embodiment, the user device 805 establishes a first connection using a first wireless communication protocol, and a second connection using a different wireless communication protocol. The first wireless connection and second wireless connection may be active concurrently, for example, if a user device is downloading a media item from a server (e.g., via the first connection) and transferring a file to another user device (e.g., via the second connection) at the same time. Alternatively, the two connections may be active concurrently during a handoff between wireless connections to maintain an active session (e.g., for a telephone conversation). Such a handoff may be performed, for example, between a connection to a WLAN hotspot and a connection to a wireless carrier system. In one embodiment, the first wireless connection is associated with a first resonant mode of an antenna building that operates at a first frequency band and the second wireless connection is associated with a second resonant mode of the antenna building that operates at a second frequency band. In another embodiment, the first wireless connection is associated with a first antenna element and the second wireless connection is associated with a second antenna element. In other embodiments, the first wireless connection may be associated with a media purchase application (e.g., for downloading electronic books), while the second wireless connection may be associated with a wireless ad hoc network application. Other applications that may be associated with one of the wireless connections include, for example, a game, a telephony application, an Internet browsing application, a file transfer application, a global positioning system (GPS) application, and so forth.

Though a modem 822 is shown to control transmission and reception via antenna (884, 888), the user device 805 may alternatively include multiple modems, each of which is configured to transmit/receive data via a different antenna and/or wireless transmission protocol.

The user device 805 delivers and/or receives items, upgrades, and/or other information via the network. For example, the user device 805 may download or receive items from an item providing system. The item providing system receives various requests, instructions and other data from the user device 805 via the network. The item providing system may include one or more machines (e.g., one or more server computer systems, routers, gateways, etc.) that have processing and storage capabilities to provide the above functionality. Communication between the item providing system and the user device 805 may be enabled via any communication infrastructure. One example of such an infrastructure includes a combination of a wide area network (WAN) and wireless infrastructure, which allows a user to use the user device 805 to purchase items and consume items without being tethered to the item providing system via hardwired links. The wireless infrastructure may be provided by one or multiple wireless communications systems, such as one or more wireless communications systems. One of the wireless communication systems may be a wireless local area network (WLAN) hotspot connected with the network. The WLAN hotspots can be created by Wi-Fi® products based on IEEE 802.11x standards by Wi-Fi Alliance. Another of the wireless communication systems may be a wireless carrier system that can be implemented using various data processing equipment, communication towers, etc. Alternatively, or in addition, the wireless carrier system may rely on satellite technology to exchange information with the user device 805.

The communication infrastructure may also include a communication-enabling system that serves as an intermediary in passing information between the item providing system and the wireless communication system. The communication-enabling system may communicate with the wireless communication system (e.g., a wireless carrier) via a dedicated channel, and may communicate with the item providing system via a non-dedicated communication mechanism, e.g., a public Wide Area Network (WAN) such as the Internet.

The user devices 805 are variously configured with different functionality to enable consumption of one or more types of media items. The media items may be any type of format of digital content, including, for example, electronic texts (e.g., eBooks, electronic magazines, digital newspapers, etc.), digital audio (e.g., music, audible books, etc.), digital video (e.g., movies, television, short clips, etc.), images (e.g., art, photographs, etc.), and multi-media content. The user devices 805 may include any type of content rendering devices such as electronic book readers, portable digital assistants, mobile phones, laptop computers, portable media players, tablet computers, cameras, video cameras, netbooks, notebooks, desktop computers, gaming consoles, DVD players, media centers, and the like.

Previous static supervised learning algorithms require the exact labeling of a training dataset in terms of whether presence is detected (e.g., motion or no motion). This exact labeling is due to a high percentage of wrong labels causing overfitting of a machine learning (ML) model or the failing of the model to converge. Channel properties data (such as CSI) may be captured at a sampling rate of between 10 Hz and 2,000 Hz. For the former, a wireless device may record a data point (e.g., a discrete sample of data) every 0.1 second and for the latter, the wireless device may record a data point every 0.5 millisecond. The labeling of data used for training the ML model is to be done in a user's home after deployment of the wireless devices discussed herein. But, in performing training in real-world scenarios, one cannot ask humans occupying a building to move for a long time, such as for five to ten minutes, in a certain way to be able to train a ML algorithm to detect presence in the building.

In embodiments, the channel properties within a communication link between wireless devices within a building may represent wireless signal propagation characteristics measured as discrete samples during a sampling period. The sampling period may be made up of a series of time frames each associated with a set of the discrete samples. The signal propagation characteristics may include a combination of scattering, fading, and power decay of wireless signals transmitted via the communication link. Processing of the channel properties data to generate a statistical parameter value may therefore be performed for each set of the discrete samples, each which corresponds to a sequential time frame of the sampling period.

FIG. 9 is a graph illustrative of time error when labeling channel properties data according to human presence, according an embodiment. Snapshots of the channel properties data waveforms are rapidly changing. For this reason, the ML algorithm may benefit from a type of automatic labeling of training data (e.g., CSI data) using information available from other sensors in the building. These sensors may include, for example, speakers, a microphone, a mobile device that provides a wireless connection that can be detected, or a motion detector such as passive infrared, microwave, ultrasonic, tomographic, video camera, gesture detection, or the like. A current wireless device such as the wireless device 200 of FIG. 2A may print a time stamp (e.g., apply a label) for captured channel properties data based on the time information contained in received data from these other sensors. The estimate of event time frames from sensor data may be precise only at the scale of a minute, so there may be labeling error for the training data as illustrated in FIG. 9. Accordingly, in real use cases, to train a ML algorithm, the training and ML algorithm are to be adaptable to an inexactly-labeled training dataset with labeling error up to a few minutes. As will be discussed in more detail, a sequential prediction model type of neural network may be selected as the ML algorithm.

FIG. 10 is a graph illustrative of intermittent sets of discrete samples within channel properties data that are indicative of non-motion, despite the presence of a human, according to one embodiment. In the training and testing stages of presence detection, there are two shortcomings in the use of channel properties data with static machine learning algorithm. First, a human may not keep moving when they are present in a room (e.g., sleeping or reading without movement). In this condition, the observed channel properties data waveform may look similar to the one in a not-present condition. In the training stage, the static algorithm may require this data (e.g., false negative type data) to be excluded and, in a testing stage, the static algorithm may predict a series of such data (as illustrated in FIG. 10) as not present. Secondly, there may be some motion in blind regions in multi-room presence detection. When a human is moving in this region, the observed channel properties data waveform may look very similar to the one in the not-present condition. Accordingly, to avoid these incorrect results (e.g., false negatives), the wireless device 200 may employ a dynamic ML algorithm that may predict presence with the use of prior inputs or states, as will be explained in detail.

Channel properties data such as WiFi® CSI may be estimated with the WiFi® signal at the frequency of either 5 GHz or 2.4 GHz band. The 2.4 GHz band shares the same frequency spectrum with that of Bluetooth® technology. If the isolation between WiFi® and Bluetooth® antennas is not greater than 25 dB, the two radios may coexist through time domain duplexing (TDD), but not through frequency domain duplexing (FDD). In other words, when 2.4 GHz WiFi® and Bluetooth® antennas exist in the same wireless device, then only one or the other may radiate electromagnetic signals at the same time. If the TDD mode is used as would be expected, the static machine learning mode fails due to the missing channel properties data during the time frames when the Bluetooth® antenna is active.

In this scenario, the overall capture of channel properties data is fragmented. A sequential prediction model type of neural network such as a long short-term memory (LSTM) neural network may be employed which can generate predictive outputs, despite the missing data, which may be used to successfully classify presence for a range of data size of CSI, for example. The classification of presence may be output from a neural network using an LSTM layer as a detection decision (e.g., as “present” or “not present”). Accordingly, if a sufficiently long sampling period is used that has a high enough percentage of channel properties data, statistical processing involved in the learning allows statistical prediction of presence that is sufficiently accurate, as illustrated and discussed in more detail with reference to FIG. 13.

FIG. 11 is a diagram of an example of a layer portion 1100 of a long short-term memory (LSTM) neural network, according to one embodiment. The LSTM neural network (NN) is an example of a recurrent neural network (RNN) that is capable of learning order dependence in sequence prediction problems (e.g., sequentially process channel properties data), and may provide deep-learning capabilities. Unlike traditional static NN, the LSTM neural network is able to remember information for long periods of time, e.g., by integration of previous states in the machine learning process.

With reference to FIG. 11, a LSTM neural network may be made up a LSTM layer of cells, where each cell that includes an input gate (i), which controls the level of cell state update, a forget gate (f), which controls the level of cell state reset (forget), a layer input (g) that adds information to a cell state value, and an output gate (o), which controls the level of cell state value added to an output state value. The state value of the layer includes the output state value (known as the hidden state value) and the cell state value. The output state at time step t contains the output of the LSTM layer for this time step (e.g., time frame). The cell state value may contain information learned from the previous time steps. At each time step, the layer adds information to or removes information from the cell state. The cell state therefore provides the reliance on previous model state values. The following equations express the values of the states of the LSTM cell, the cell state value (c_(t)), and the output state value (h_(t)):

$\begin{matrix} {{\begin{pmatrix} i \\ f \\ o \\ g \end{pmatrix} = {\begin{pmatrix} \sigma \\ \sigma \\ \sigma \\ \tanh \end{pmatrix}{W\begin{pmatrix} h_{t - 1} \\ x_{i} \end{pmatrix}}}},} & (15) \end{matrix}$ c _(t) =f⊙c _(t-1) +i⊙g,  (16) h _(t) =o⊙ tan h(c _(t)).  (17)

FIG. 12 is a flow chart of a method 1200 for training a sequential prediction model type of NN (e.g., LSTM NN) and for classification of channel properties data using the trained sequential prediction model type of NN, according to various embodiments. The method 1200 may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (such as instructions running on the processor), firmware or a combination thereof. In one embodiment, a processor of the computing device 150 or the wireless device 200 performs the method 1200. Alternatively, other components of the user device may perform some or all of the operations of the method 1200. The following features of the method 1200 are exemplary and additional and different features of the method 1200 will be described with reference to later figures.

With reference to FIG. 12, in a training stage 1201, the method 1200 may begin with the processing logic receiving channel properties data (e.g., CSI and/or RSSI data) (1204). The method 1200 may continue with the processing logic triggering an event by receipt of sensor data that includes time stamps or other timing information, and thus confirmation of presence of a human (1208). The method 1200 may continue with the processing logic starting to record channel properties data, which is ordered sequentially with time, with automatic labeling using the sensor data (1212). In one embodiment, the labeling may be performed on each set of discrete samples of the channel properties data, or on a group of sets of the discrete samples. The method 1200 may continue with the processing logic stacking input vectors (e.g., one-dimensional (1D) input vectors) with statistical parameter (or feature) values generated from sets of discrete samples of the channel properties data within a sampling period and for multiple subcarriers (1216). To stack may indicate to sequentially populate the input vectors. The statistical parameters may include, related to the labeled training data generated at block 1212, at least one of a Fast Fourier Transform (FFT) value, a maximum value, a minimum value, a mean value, a variance value, an entropy value, a mean cross rate value, a skewness value, or a kurtosis value.

The method 1200 may continue with the processing logic randomly varying the length of data in at least some of the input vectors to make input vectors of varying length (1220), which results in a labeled training dataset 1230. The varying the input vectors may be performed in various ways, such as by dropping varying amounts of the channel properties data from some of the input vectors. In another embodiment, the input vectors may be varied in sequence length by partitioning a stream of statistical data into multiple statistical data portions of different sequence lengths, and stacking respective input vectors with statistical data portions of an identical sequence length, e.g., with data for an identical number of time frames. The method 1200 may continue with the processing logic training, via machine learning (1236) using the labeled training dataset, a sequential prediction model type of neural network such as a LSTM neural network model 1240.

With continued reference to FIG. 12, in a classification stage 1251, the method 1200 may begin with the processing logic receiving channel properties data (e.g., CSI and/or RSSI data) (1254). The method 1200 may continue with the processing logic performing pre-processing (similarly as done with in the training stage 1201), including stacking 1D input vectors with statistical parameter (feature) values generated from sets of discrete samples over a classification time period and for multiple subcarriers (1266). The method 1200 may continue with the processing logic randomly varying the sequence length of data in at least some of the input vectors to make input vectors of varying sequence length (1268). The varying the length of input vectors may be performed in a variety of ways as discussed with reference to block 1220.

The method 1200 may continue with the processing logic sequentially feeding the input vectors to the LSTM neural network model 1240, as previously trained during the training stage 1201. The method 1200 may continue with the processing logic classifying the channel properties data represented by the statistical parameter values in the input vectors for the sampling time period as present or not present (1278). The classifying may take the form of a series of predictive outputs, e.g., a predictive output for each set of the discrete samples of the channel properties data. Note that the output of the LSTM neural network model 1240 may feed back into the LSTM neural network model 1240 so that current output states may be defined by both input state values and past state values, as discussed with reference to FIG. 11. The method 1200 may complete with the processing logic outputting a detection decision (e.g., present or not present) (1284). A detection decision of present may be made in response to a threshold number of a series of sets of discrete samples of channel properties data being classified as present at block 1278 of the method 1200, which will be discussed in more detail with reference to FIG. 13.

Note that in alternative embodiments, blocks 1212 through 1220 and 1266 and/or blocks 1266 to 1270 of the method 1200 of FIG. 12 may include further pre-processing of the channel properties data and/or the labeled training data. Such pre-processing may include additional or different steps as will be discussed with reference to FIGS. 17-21. In various embodiments, the method 1200 may also include statistical processing of the channel properties data to generate the statistical parameter, or feature, values for each set of discrete samples that may be further pre-processed before being input into the LSTM neural network model for either learning (in the training stage 1201) or classification (in the classification stage 1251). Specifically, the statistical parameter values may be useable as feature values to define the LSTM neural network model 1240. By using the values of one or more statistical features, the classifier of the LSTM neural network model 1240 may improve classification accuracy. Each statistical parameter value may be representative of the propagation characteristics of a set of discrete samples of the channel properties data. In various embodiments, the statistical parameter values may come from, related to respective sets of discrete samples of the channel properties data, one or more of a FFT value, a maximum value, a minimum value, a mean value, a variance value, an entropy value, a mean cross rate value, a skewness value, or a kurtosis value.

In various embodiments, the maximum and minimum values per subcarrier may be the maximum and minimum value of magnitude of complex CSI h(k, t_(n)) over time period T, e.g., a time frame of a period of seconds or fraction of seconds. The mean value of h(k, t_(n)) per subcarrier over time period T may be defined as

$\begin{matrix} {{\mu(k)} = {\frac{1}{N}{\sum\limits_{t_{n} \in T}{{h\left( {k,t_{n}} \right)}}}}} & (18) \end{matrix}$

In one embodiment, the variance value per subcarrier may be defined as

$\begin{matrix} {{{var}(k)} = {\frac{1}{N - 1}{\sum\limits_{N}\left\lbrack {{{h\left( {k,t_{n}} \right)}} - \mu} \right\rbrack^{2}}}} & (19) \end{matrix}$ where there are N samples for the time period T.

The magnitude of h(k, t_(n)) over time period T may have N samples and can construct a N_(sc)×N matrix H like a two-dimensional image, given in Equation (20).

$\begin{matrix} {H = \begin{bmatrix} {{h\left( {1,t_{1}} \right)}} & \ldots & {{h\left( {1,t_{N}} \right)}} \\ \vdots & \ddots & \vdots \\ {{h\left( {N_{sc},t_{1}} \right)}} & \ldots & {{h\left( {N_{sc},t_{N}} \right)}} \end{bmatrix}} & (20) \end{matrix}$ From the matrix, H, the processing logic may remove the stationary objects within the scene through the subtraction of the mean of sampled time period (H_(mean)) and normalize the data to a grayscale image (all entries are between 0 and 1) I_(gray).

In one embodiment, the processing logic CSI entropy value may be computed with the formula of the image entropy

$\begin{matrix} {{Entropy} = {- {\sum\limits_{{All}\;{({m,n})}}{{I_{gray}\left( {m,n} \right)}*{\log_{2}\left( {I_{gray}\left( {m,n} \right)} \right)}}}}} & (21) \end{matrix}$

where (m, n) is the entry of m_(th) row and n_(th) column of gray scale image. Mean cross rate value per subcarrier may count the number of crossings (in the positive direction) of h(k, t_(n)) through the mean valued μ for the specified time period, T.

Skewness is a measure of the asymmetry of the data around the sample mean. If skewness is negative, the data are spread out more to the left of the mean than to the right. If skewness is positive, the data are spread out more to the right. The skewness value of the normal distribution (or any perfectly symmetric distribution) is zero.

In one embodiment, therefore, the skewness feature per subcarrier may be defined as expressed in Equation (22).

$\begin{matrix} {{s(k)} = \frac{\frac{1}{N}{\sum\limits_{N}\left\lbrack {{{h\left( {k,t_{n}} \right)}} - \mu} \right\rbrack^{3}}}{\left\{ \sqrt{\frac{1}{N}{\sum\limits_{N}\left\lbrack {{{h\left( {k,t_{n}} \right)}} - \mu} \right\rbrack^{2}}} \right\}^{3}}} & (22) \end{matrix}$

Kurtosis is a measure of how outlier-prone a distribution is of a dataset. The kurtosis value of the normal distribution is three. Distributions that are more outlier-prone than the normal distribution have a kurtosis value greater than three, e.g., distributions that are less outlier-prone have kurtosis values less than three. Kurtosis may be expressed by the following Equation (23).

$\begin{matrix} {{{Kr}(k)} = \frac{\frac{1}{N}{\sum\limits_{N}\left\lbrack {{{h\left( {k,t_{n}} \right)}} - \mu} \right\rbrack^{4}}}{\left\{ {\frac{1}{N}{\sum\limits_{N}\left\lbrack {{{h\left( {k,t_{n}} \right)}} - \mu} \right\rbrack^{2}}} \right\}^{3}}} & (23) \end{matrix}$

FIG. 13 is a first block diagram of LSTM architecture 1300 for presence detection according to one embodiment. A series of channel properties data for a sampling period 1301 (which may be the time period T) may be used to predict presence status, e.g., to generate predictive outputs. The channel properties data for the time period 1301 may be divided into eight sets of discrete samples, each set corresponding to a slice or time frame of the sampling period. In various embodiments, a variance value (or other statistical parameter value) generated from each set of discrete samples may be calculated for each subcarrier of the communication link between a first wireless transceiver and a second wireless transceiver. These statistical parameter values associated with each time frame are input into a separate LSTM cell, which as discussed with reference to FIG. 11, may generate an output state value ({right arrow over (h)}_(n)) and a cell state value that is forwarded to the next LSTM cell. Each output state value may be a hidden state value that goes into a layer of hidden states 1320.

Note that making a detection decision based on a whole group of predictive outputs (e.g., associated with the series of time frames) over the sampling period 1301 means that some of the sets of discrete samples may be missing or corrupted during some of the time frames and the machine learning or presence classification may still be successfully predicted. In the illustrated example of FIG. 13, channel properties data of at least one of the sets of discrete samples may be indicative that a human is not present in the building, and still result in successful detection decision (e.g., a “present” detection decision). Indeed, the two or three of the eight sets of discrete samples may be missing from the channel properties data without causing presence prediction to fail. Accordingly, when a threshold number of sets of discrete samples (e.g., 60%, 70%, or other percentage) are classified as present, the LSTM architecture 1300 may result in a detection decision of “present” over the sampling period.

FIG. 14 is a second block diagram of the LSTM architecture 1300 for presence detection according to one embodiment. The LSTM architecture 1300 may further include an output layer 1330 that includes a fully-connected layer 1334 and a software max layer 1338. In various embodiments, the fully-connected layer 1334 may generate a predictive output that is an analog signal (or other comparable value in software) based on the learned state from hidden state values 1320 of the LSTM layer. A predictive output, which may be a value between zero (“0”) and one (“1”), may be generated by the fully-connected layer 1224 for each set of discrete samples of the channel properties data, for example. The software max layer 1338 may include a threshold detector that detects the analog signal as a zero or one based on a comparison to some threshold value (e.g., over 0.50 is a one) and generates a detection decision based on a combination of the predictive outputs over the sampling period. This allows the LSTM architecture 1300 to turn continuously-varying numbers into one of a number of finite classes (e.g., predictive outputs), and make a detection decision based on a combination of prediction outputs.

In general, human motion causes the disturbance of the channel properties data and higher variance. When the motion is not observed, e.g., the human sits or stands still for some time, variance may drop low enough to indicate a not-present status. In the training dataset, the human motion may come with the intermittent snapshots of non-motion, as was discussed. This type of data series may be labeled as present based on the triggered event. In the not-present case, an entire room of a building may be motion-free. A series of set of discrete samples within the channel properties data may be labeled as not-present based on a triggered event. By learning from automatically-labeled training data, false detection may be eliminated by observing a series of channel properties data over a larger sampling period, T. A similar advantage is obtained by classification of channel properties of a larger time window using a LSTM layer within a NN.

It is also noted that the length of input time frames may not be a constant length, e.g., length N, due to the fact that LSTM cells incorporate past states and may therefore predict presence even in the absence of data. In spite of time sharing with other radio protocols (such as Bluetooth® or Zigbee®), the lost channel properties data may therefore be skipped. The input length from N-M to N may be used to detect presence correctly.

FIG. 15 is a graph indicative of a subcarrier index of presence detection decisions over a series of sequential time frames, according to one embodiment. In the illustrated example, there are 12 subcarriers, each graphed with a sequential series of time frames. In other embodiments, there may be more subcarriers, for example, 16, 32, 64, 128, and the like. The upper sequential lines represent a sequential input vector that indicates not-present status. The lower sequential lines represent a sequential input vector that indicates present status. The two input training vectors for each subcarrier may have different lengths, wherein missing data is skipped and the input vector thus compressed. In the example of FIG. 15, each time frame contains a statistical parameter value over a two-second time frame of channel properties data, although different time frame lengths are envisioned. The maximum length of the input vector to the LSTM neural network may be about 20 time frames, e.g., 30 seconds of channel properties data, although different sampling time periods are envisioned.

FIG. 16A is a graph indicative of channel properties data magnitude over time with a subcarrier index of 10, according to one embodiment. Note that sequential mode prediction may respond quickly for “present” status and slower for “not-present” status, as can be seen in the two transitions illustrated in the graph of FIG. 16A. So, for example, there may be one time frame delay to detect a present status, while there may be a two time frame delay to detect not present status. This may because it may be more important to detect human presence quicker than the absence of presence, e.g., to turn on a light when a human walks in a room more quickly than to turn off the light when the human leaves the room.

FIG. 16B is a graph indicative of classification of presence of the channel properties data illustrated in the graph of FIG. 16A, according to one embodiment. The training data may tolerate several intermittent periods of motion-free human behavior, so that the automatic labeling is enabled despite these intermittent periods. As illustrated, the prediction of presence status is binary, and in this example, is either zero (not present) or one (present), which detection decision may still be made despite intermittent periods of prediction outputs of “not present.”

FIG. 16C is a graph indicative of classification confidence related to the classification of the presence as illustrated in the graph of FIG. 16B, according to one embodiment. Note that the closer the output from the LSTM training is to a one (“1), the higher the confidence that the output was classified correctly as “present.” Similarly, the closer the output from the LSTM layer is to a zero (“0”), the higher the confidence that the output was classified correctly as “not present.” In the testing stage 1251, the sensitivity of the LSTM neural network model to a short time of a motion-free condition may be mitigated by learning from previous states. In one embodiment, processing logic may determine a difference between a value (e.g., zero for non-presence or one for presence) and each of the plurality of hidden state values from the LSTM layer. The processing logic may further generate a presence confidence level based on how close the differences are to a threshold value, e.g., how close 0.50 for example. The processing logic may further output the presence confidence level associated with a determination that the human is present in the building (or is not present in the building).

FIG. 17 is a flow diagram 1700 that illustrates preprocessing of the channel properties data before input into the sequential prediction model type of NN for training or classification, according to one embodiment. The preprocessing may be performed in various steps, after the stream of channel properties data has been statistically processed. For example, in a first step 1710, processing logic may partition the statistically-processed CSI data into a number of sequences (e.g., statistical data portions) with varying length, e.g., of a sequence length between N_(min) to N_(max). In an alternative embodiment, the channel properties data may first be partitioned as illustrated at step 1710, followed by statistically processing of the partitioned data stream.

At a second step 1720, the processing logic may stack an input vector (X_train) with the statistical data portions for each subcarrier, to generate a plurality of input vectors. At a third step 1730, the processing logic may populate another input vector (Y_train) with presence decisions, e.g., with a one for present or a zero for not-present status for respective statistical data portions. The X_train vector and the Y_train vector may be linked together as illustrated to form the labeled training dataset 1230. In one embodiment, the processing logic may, at a fourth step 1740, execute grouping of the plurality of statistical data portions within the labeled training dataset into sub-groups according to the sequence lengths of the statistical data portions. In one embodiment, the grouping may be performed by stacking a plurality of input vectors with statistical data portions of the plurality of statistical data portions of an identical sequence length of time frames to generate a plurality of input vectors of varying length, e.g., a varying number of time frames. For example, the statistical data portions may be ordered and grouped according to gradient descent before being sent to the LSTM neural network for training or classification, thus providing for more robust convergence of the machine learning while avoiding local minima.

Note that each training sample may contain the statistics of many time frames of the sampling period and each time frame may include a set of discrete CSI samples, e.g., one-second observations or some other time period for each time frame. A different sampling period may correspond to a different number of sets of discrete samples, but a presence prediction may be performed for each set of the discrete samples, to generate a predictive output for each set.

FIG. 18 is a block diagram of classification of received channel properties data using a trained sequential prediction model type of NN (e.g., a LSTM NN layer), according to one embodiment. In the testing stage 1251, the prediction may be made per time frame, without waiting for the data of an entire sample size. The initial states may be random values but, after a few time frames, the recursive process of ML learning may fill in the initial states automatically. The current hidden state values may then replace the prior state values in the next time frame. The oldest state values may be dropped. In this way, the prediction accuracy may converge after a few time steps or frames.

FIG. 19 is a block diagram that illustrates compression of input vectors (containing statistical values) through processing the input vectors 1902 with a multi-layered, unsupervised learning auto-encoder network (e.g., SAE network 1900), according to one embodiment. Recall that the input vectors 1902 may contain a number of statistical parameter values, and thus may contain much data that needs to be processed by the LSTM layers of the neural network for training and classification, which requires significant CPU resources and time. To help reduce the dimensions of the statistical parameter data (e.g., reduce the feature vector size of the input vectors), and thus alleviate the processing requirements for LSTM, the SAE network 1900 may be employed.

In some embodiments, during the SAE network processing, statistical parameter values with more importance to the LSTM-based learning (e.g., variance values) may be prioritized over other statistical parameter values with less importance (e.g., kurtosis values). The prioritization may be implemented with different weightings of the statistical value parameters as the input vectors are processed by the SAE network 1900. For example, the processing logic may execute the SAE network to apply a first weight value to a first statistical parameter value and apply a second weight value to a second statistical parameter value. The first weight value may be made to be greater than the second weight value to emphasize the first statistical parameter value over the second statistical parameter value. This application of a higher weight value to the first statistical parameter value as compared to the second statistical parameter value may provide for prioritization of the first statistical parameter within the SAE network.

The illustrated SAE network 1900 may be sparse, which may be a high performance, deep learning network suitable for dealing with one-dimensional data, to extract optimized deep image features. Compared with traditional neural networks, SAE networks may learn the initial network parameters with an unsupervised manner, which overcomes the initialization problem and simplifies the network training procedure.

With additional reference to the block diagram of FIG. 19, the disclosed SAE network 1900 may include three encoders, Encoder 1, Encoder 2, and Encoder 3, where each encoder may be made up of a different number of neurons, and three decoders, Decoder 1, Decoder 2, and Decoder 3, which each decoder may up with a different number of neurons. In various embodiments, neurons are also called processing nodes. The output of each encoder includes different weighted values applied to respective statistical parameter values and in a way that incrementally reduces the dimension of the data at each SAE network layer, or in the case of a 1D input vector, compresses the data into a smaller size without loss of information in the compressed data. By reducing the dimensions or compressing the input vectors slowly, lost information may be minimized.

In the simplest case, where there is one hidden layer, the encoder stage of the SAE network 1900 may take the input {right arrow over (Xn)} and encode the input with an n_(th) layer of fully-connected neurons {right arrow over (Zn)}=f({right arrow over (Wn Xn)}). This {right arrow over (Zn)} is usually referred to as code, which has a smaller feature dimension than input {right arrow over (Xn)}. The decoder stage of the SAE network 1900 may then map {right arrow over (Zn)} to the reconstructed outputs, {right arrow over (Xn′)}=f(Wn′Zn). Auto-encoders are also trained to minimize reconstruction errors (such as squared errors) as follows: L=∥({right arrow over (Xn)}−{right arrow over (Xn′)}∥²,  (24) where {right arrow over (Xn)} may be averaged over some input training set without any label.

The SAE network 1900 may be trained layer by layer to gradually reduce the input feature vector dimension. The output of the (n−1)_(th) layer encoder may be the input of the n_(th) layer of the SAE network 1900 for training. In the present case, the SAE network 1900 has three layers of fully-connected neurons. The encoded output may be the input to the sequential input layers of the LSTM neural network 1240. Because the input size is much smaller and clustered after compression, the training or prediction process may be much faster.

In one embodiment, the processing logic may execute the SAE network to process multiple input vectors with a first encoder having a group of processing nodes and a first set of weights associated with the statistical parameter values to generate a first intermediate set of input vectors of smaller dimension than that of the multiple input vectors. The processing logic may then further execute the SAE network to process the first intermediate set of input vectors with a second encoder having a second group of processing nodes, which are fewer than the first group of processing nodes, and a second set of weights to generate a second intermediate set of input vectors of smaller dimension than that of the first intermediate set of input vectors. The processing logic may further execute the SAE network to process the second intermediate set of input vectors with a third encoder having a third group of processing nodes, which are fewer than the second group of processing nodes, and a third set of weights to generate a third intermediate set of input vectors of smaller dimension than that of the second intermediate set of input vectors. The LSTM layer of the neural network may then process each third intermediate value in lieu of the data originally presented within the input vectors.

The following is provided by way of example only for explanation of how the input vector data may have dimensions decreased or to compress 1D vectors into smaller vectors.

1. Assume a training data set X1 with N samples and the jth sample is a 1D vector X1{j} with length (or width) D (where “D” stands for dimension). The processing logic may build Encoder 1 and Decoder 1 with D=6, and “one” is the bias so is not included. The encoded data may be H1{j}=W₁X1{j} and the decoded data X1′{j}=W_(1′)H(1){j}. The encoder and decoder weight vector W1 and W1′ may be found through minimizing the squared error L{j}=∥X1{j}−X1′{j}∥^2 for all samples.

2. Next, processing logic may train Encoder 2 and Decoder 2 in the same way by taking encoded data H1 with smaller dimension (now D=4) as input to the Encoder 2 of the SAE network 1900. The encoded data may be H2{j}=W₂H1{j} and the decoded data H1′{j}=W₂′H2{j}. The encoder and decoder weight vector W2 and W2′ may be found through minimizing the squared error L{j}=∥H1{j}−H1′{j}∥^2 for all samples.

3. Next, the processing logic may use the H2 (now the dimension is only 3) as the input to Encoder 3, the third encoder, and find the weight matrix W3 and W3′ as already explained with reference to the first and second encoder/decoder layers.

4. The processing logic may then cascade the three encoders with weights W1, W2, and W3 into three layers of neural network for the future use of LSTM input compression. Now suppose the input layer of X1 has data size of D. After passing through the SAE network, the new input vector Xnew with a much smaller dimension than the previous input vector. The processing logic may then fix the weight matrix W1, W2, W3 and use the new input vector Xnew for training the LSTM neural network model similarly as performed with a single statistical parameter for the input vectors.

FIG. 20 is a flow diagram that illustrates where the compression of the input vectors (illustrated in FIG. 19) may be sequenced within a method 2000 for classification of channel properties data to detect human presence, according to one embodiment. Note that the SAE network 1900 may include the three encoders (Encoder 1, Encoder 2, and Encoder 3), which may be positioned between the input feature vectors (containing statistical parameter values), and the LSTM layer that include the LSTM machine learning or classification. The other layers were already discussed and their explanation is not repeated here.

FIG. 21 is a flow chart of a method 2100 for automatic labeled supervised learning using a sequential prediction model type of neural network, according to various embodiments. The method 2100 may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (such as instructions running on the processor), firmware or a combination thereof. In one embodiment, a processor of the computing device 150 or the wireless device 200 performs the method 2100. Alternatively, other components of the user device may perform some or all of the operations of the method 2100. The following features of the method 2100 are exemplary as some may be optional and other and different features described herein may be added.

With reference to FIG. 21, the method 2100 may begin with the processing logic receiving time-sequenced channel properties data (e.g., CSI data) indicative of channel properties of a first communication link between a first wireless transceiver in a first device and a second wireless transceiver in a second device, the first device and the second device being located in a building (2110A). In one embodiment, the channel properties represent wireless signal propagation characteristics measured as discrete samples during a sampling period. The sampling period may include a series of time frames each associated with a set of the discrete samples. The signal propagation characteristics may include a combination of scattering, fading, and power decay of wireless signals transmitted via the communication link. At the same time, the method may include the processing logic receiving sensor data form one or more sensors also located in the building (2110B). The method 2100 may continue with the processing logic labeling a series of sets of discrete samples of the first data as present based on indications of presence of a human in the building within corresponding time frames of the sensor data, to generate labeled training data (2115). The method 2100 may continue with the processing device pre-processing the labeled training data to generate a statistical parameter value associated with each set of discrete samples and for a plurality of subcarriers associated with the first communication link, to generate a stream of statistical data over the sampling period (2120).

With continued reference to FIG. 21, the method 2100 may continue with the processing logic partitioning the stream of statistical data to generate a plurality of statistical data portions each of a different sequence length (2125). The method 2100 may continue with the processing logic stacking an input vector with the statistical data portions for each subcarrier, to generate a plurality of input vectors (2130). The method 2100 may continue with the processing logic grouping the plurality of statistical data portions within the labeled training dataset into sub-groups according to identical sequence lengths of the statistical data portions, to generate labeled training data (2135). The method 2100 may continue with the processing logic compressing the input vectors via processing input vectors with a multi-layered, unsupervised learning auto-encoder (SAE) network (2140). The method 2100 may continue with the processing logic training, using the labeled (and optionally compressed) training dataset, the LSTM neural network to detect the presence of humans within the building during separate classification periods (2145). Note that the steps of blocks 2120 through 2140 may also before performed during classification.

FIG. 22 is a flow chart of a method 2200 for generating a detection decision using a trained sequential prediction model type of NN, e.g., a LSTM-based NN, according to an embodiment. The method 2200 may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (such as instructions running on the processor), firmware or a combination thereof. In one embodiment, a processor of the computing device 150 or the wireless device 200 performs the method 2200. Alternatively, other components of the user device may perform some or all of the operations of the method 2200. The following features of the method 2200 are exemplary as some may be optional and other and different features described herein may be added.

With reference to FIG. 22, the method 2200 may begin with the processing logic receiving, for sampling period, time-sequenced channel properties data (e.g., CSI data) indicative of channel properties of a first communication link between a first wireless transceiver in a first device and a second wireless transceiver in a second device, the first device and the second device being located in a building (2210). In one embodiment, the channel properties represent wireless signal propagation characteristics measured as discrete samples during the sampling period. The sampling period may include a series of time frames each associated with a set of the discrete samples. The signal propagation characteristics may include a combination of scattering, fading, and power decay of wireless signals transmitted via the communication link.

The method 2200 may continue with the processing logic pre-preprocessing the CSI data to generate a statistical parameter value for each set of discrete samples of the CSI data over the sampling period to generate a stream of statistical data (2220). In one embodiment, at least one set of the discrete samples of the channel properties data indicates that the human is not present in the building. The method 2200 may continue with the processing logic stacking (e.g., sequentially populating) a plurality of input vectors with statistical data of a sequential time period of the stream of the statistical data, to generate multiple input vectors (2230). The method 2200 may continue with the processing logic randomly varying the sequence length of the data in at least some of the multiple input vectors to make the input vectors of varied sequence lengths (2240). The randomly varying the sequence length may be performed in various ways as discussed previously with respect to FIG. 12. The method 2200 may continue with the processing logic compressing data of the multiple input vectors via processing the input vectors with a multi-layered, unsupervised learning auto-encoder network, to facilitate faster processing of the multiple input vectors by a neural network (2250).

With continued reference to FIG. 22, the method 2200 may continue with the processing logic processing, by a long short-term memory (LSTM) layer of the neural network, the multiple input vectors to generate multiple of hidden state values of the neural network (2260). The method 220 may continue with the processing logic processing, by a fully-connected layer of the neural network, respective hidden state values of the multiple hidden state values to generate predictive outputs, each representative of presence prediction as to whether a human is present in the building (2270). The method 2200 may continue with the processing logic processing, by a softmax layer of the neural network, the predictive outputs from the fully-connected layer to generate a detection decision that indicates presence of the human in the building (2280).

As a further example of the embodiments of FIGS. 21 and 22, related to block 2220, the processing logic may pre-process a first set of the discrete samples of the sampling period to generate a first statistical parameter value representative of propagation characteristic of the first set of discrete samples; pre-process a second set of the discrete samples of the sampling period to generate a second statistical parameter value representative of propagation characteristic of the second set of discrete samples; and pre-process a third set of the discrete samples of the sampling period to generate a third statistical parameter value representative of propagation characteristic of the third set of discrete samples. The processing logic may further, with continued reference to blocks 2120 and 2220, generate a stream of statistical data associated with the series of time frames, the stream of statistical data including the first statistical parameter value, the second statistical parameter value, and the third statistical parameter value. The processing logic may further, with reference to block 2125, partition the stream of statistical data into a first statistical data portion of a first sequence length including at least the first statistical parameter value and the second statistical parameter value; and into a second statistical data portion of a second sequence length including at least the third statistical parameter value, where the first sequence length is longer than the second sequence length.

The processing logic may further, with reference to blocks 2130 and 2230, stack a first input vector with at least the first statistical data portion of the first sequence length and a second input vector with at least the second statistical data portion of the second sequence length to generate multiple input vectors of varying length. The processing logic may, with reference to blocks 2145 and 2260, sequentially process, using a long short-term memory (LSTM) layer of the neural network, data of the multiple input vectors to generate multiple hidden state values of the neural network. The processing logic may further, with reference to blocks 2270 and 2280, sequentially feed the multiple hidden state values into a set of additional neural network layers (e.g., the fully-connected layer and the software layer) of the neural network to generate a detection decision that a human is present within the building. Accordingly, pre-processing steps between the method 2200 of FIG. 21 and the method 2200 of FIG. 22 may be intermixed in various additional embodiments and be within the scope of present description.

In the above description, numerous details are set forth. It will be apparent, however, to one of ordinary skill in the art having the benefit of this disclosure, that embodiments may be practiced without these specific details. In some instances, well-known buildings and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the description.

Some portions of the detailed description are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers or the like.

It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as “inducing,” “parasitically inducing,” “radiating,” “detecting,” determining,” “generating,” “communicating,” “receiving,” “disabling,” or the like, refer to the actions and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (e.g., electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

Embodiments also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CD-ROMs and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions.

The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct a more specialized apparatus to perform the required method steps. The required building for a variety of these systems will appear from the description below. In addition, the present embodiments are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein. It should also be noted that the terms “when” or the phrase “in response to,” as used herein, should be understood to indicate that there may be intervening time, intervening events, or both before the identified operation is performed.

It is to be understood that the above description is intended to be illustrative, and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reading and understanding the above description. The scope of the present embodiments should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. 

What is claimed is:
 1. A computing device comprising: a communication interface; a storage device to store code of a neural network; and a processor coupled to the storage device and the communication interface, wherein the processor is to: receive, from a first wireless transceiver, channel properties of a communication link between the first wireless transceiver and a second wireless transceiver located in a building with the wireless transceiver, wherein the channel properties represent wireless signal propagation characteristics measured as discrete samples during a sampling period, and wherein the signal propagation characteristics comprise a combination of scattering, fading, and power decay of wireless signals transmitted via the communication link between the first wireless transceiver and the second wireless transceiver; pre-process a first set of the discrete samples within the sampling period to generate a first statistical parameter value representative of the propagation characteristics of the first set of discrete samples; pre-process a second set of the discrete samples within the sampling period to generate a second statistical parameter value representative of the propagation characteristics of the second set of discrete samples; pre-process a third set of the discrete samples within the sampling period to generate a third statistical parameter value representative of the propagation characteristics of the third set of discrete samples; generate a stream of statistical data comprising the first statistical parameter value, the second statistical parameter value, and the third statistical parameter value; partition the stream of statistical data into: a first statistical data portion of a first sequence length comprising at least the first statistical parameter value and the second statistical parameter value; and a second statistical data portion of a second sequence length comprising at least the third statistical parameter value, wherein the first sequence length is longer than the second sequence length; generate a first input vector that includes the first statistical data portion and a second input vector that includes the second statistical data portion; sequentially process, using a long short-term memory (LSTM) layer of the neural network, data of the first and the second input vectors to generate a plurality of hidden state values of the neural network; and sequentially feed the plurality of hidden state values into a set of additional neural network layers of the neural network to determine that a human is present within the building.
 2. The computing device of claim 1, wherein at least one of the first set, the second set, or the third set of the discrete samples indicates lack of human presence in the building.
 3. The computing device of claim 1, wherein to determine that the human is present, the processor is to further execute: a fully-connected layer of the neural network that generates, from respective hidden state values of the plurality of hidden state values, at least a first predictive output, a second predictive output, and a third predictive output, each representative of a presence prediction of whether the human is present within the building; and a softmax layer of the neural network that determines presence of the human based on a combination of the first predictive output, the second predictive output, and the third predictive output.
 4. The computing device of claim 1, wherein the processor is further to: determine a difference between a value of one and each of the plurality of hidden state values; generate a presence confidence level based on how close the differences are to a threshold value; and output the presence confidence level associated with a determination that the human is present in the building.
 5. A method comprising: receiving, by a computing device from a first wireless transceiver of a first device, first data indicative of channel state information (CSI) of a first communication link between the first wireless transceiver of the first device and a second wireless transceiver of a second device, the first device and the second device being located in a building; pre-preprocessing, by the computing device, the first data to generate a plurality of input vectors composed of statistical parameter values derived from sets of discrete samples of the first data; processing, by a long short-term memory (LSTM) layer of a neural network executed by the computing device, the plurality of input vectors to generate a plurality of hidden state values of the neural network; and processing, by a set of additional layers of the neural network executed by the computing device, respective hidden state values of the plurality of hidden state values to determine that a human is present in the building.
 6. The method of claim 5, wherein processing by the set of additional layers of the neural network comprises executing: a fully-connected layer of the neural network to generate, from respective hidden state values of the plurality of hidden state values, a series of predictive outputs, each representative of presence prediction; and a softmax layer of the neural network that determines presence of the human based on the presence prediction of a combination of the series of predictive outputs.
 7. The method of claim 5, further comprising: determining a difference between a value of one and each of the plurality of hidden state values; generating a presence confidence level based on how close the differences are to a threshold value; and outputting the presence confidence level associated with a determination that the human is present in the building.
 8. The method of claim 5, wherein pre-processing the first data comprises: sequentially pre-processing the sets of discrete samples of the first data to generate a statistical parameter value associated with each set of discrete samples and for a subcarrier associated with the first communication link, to generate a stream of statistical data; stacking the plurality of input vectors with statistical data from a sequential time period of the stream of the statistical data; and randomly dropping some of the statistical data within at least some of the plurality of input vectors, to vary lengths of the plurality of input vectors.
 9. The method of claim 5, further comprising processing the plurality of input vectors with a multi-layered, unsupervised learning auto-encoder network, including: applying a first weight value to a first statistical parameter value, wherein the first statistical parameter value comprises a variance value; and applying a second weight value to a second statistical parameter value, wherein the second statistical parameter value is not a variance value, wherein the first weight value is greater than the second weight value.
 10. The method of claim 5, further comprising processing the plurality of input vectors with a multi-layered, unsupervised learning auto-encoder network, including: processing the plurality of input vectors with a first encoder having a first plurality of processing nodes and a first set of weights associated with the statistical parameter values to generate a first intermediate set of input vectors of smaller dimension than that of the plurality of input vectors; processing the first intermediate set of input vectors with a second encoder having a second plurality of processing nodes, which are fewer than the first plurality of processing nodes, and a second set of weights, to generate a second intermediate set of input vectors of smaller dimension than that of the first intermediate set of input vectors; and processing the second intermediate set of input vectors with a third encoder having a third plurality of processing nodes, which are fewer than the second plurality of processing nodes, and a third set of weights to generate a third intermediate set of input vectors of smaller dimension than that of the second intermediate set of input vectors, wherein the LSTM layer of the neural network processes the third intermediate value.
 11. The method of claim 5, wherein at least one set of the sets of discrete samples of the first data indicates lack of human presence in the building.
 12. The method of claim 5, wherein the statistical parameter values comprises one or more of, related to respective sets of discrete samples of the first data, a Fast Fourier Transform value, a maximum value, a minimum value, a mean value, a variance value, an entropy value, a mean cross rate value, a skewness value, or a kurtosis value.
 13. A non-transitory computer-readable medium comprising instructions which, when executed by a computing device in communication with a first device and a second device located in a building, causes the computing device to perform operations comprising: retrieving first data received from a first wireless transceiver of the first device, wherein the first data is indicative of channel properties of a first communication link between the first wireless transceiver of the first device and a second wireless transceiver in the second device; pre-preprocessing the first data to generate a plurality of input vectors composed of statistical parameter values derived from sets of discrete samples of the first data; processing, by a long short-term memory (LSTM) layer of a neural network, the plurality of input vectors to generate a plurality of hidden state values of the neural network; executing a fully-connected layer of the neural network to generate, from respective hidden state values of the plurality of hidden state values, a series of predictive outputs, each representative of presence prediction; and executing a softmax layer of the neural network that is to determine that a human is in the building based on the presence prediction of a combination of the series of predictive outputs.
 14. The non-transitory computer-readable medium of claim 13, wherein the retrieving the first data comprises retrieving channel state information (CSI) stored at the first device, at the second device, or at both.
 15. The non-transitory computer-readable medium of claim 14, wherein at least one set of the sets of discrete samples of the first data comprises CSI data indicative of lack of human presence in the building.
 16. The non-transitory computer-readable medium of claim 13, wherein pre-processing the first data comprises: sequentially pre-processing the sets of discrete samples of the first data to generate a statistical parameter value associated with each set of discrete samples and for a subcarrier associated with the first communication link, to generate a stream of statistical data; partitioning the stream of statistical data into a plurality of statistical data portions, each of a different data sequence length; and stacking each of the plurality of input vectors with statistical data portions, of the plurality of statistical data portions, of an identical data sequence length to generate the plurality of input vectors.
 17. The non-transitory computer-readable medium of claim 13, wherein the operations further comprise: determining a difference between a value of one and each of the plurality of hidden state values; generating a presence confidence level based on how close the differences are to a threshold value; and outputting the presence confidence level associated with a determination that the human is present in the building.
 18. The non-transitory computer-readable medium of claim 13, wherein the operations further comprise processing the plurality of input vectors with a multi-layered, unsupervised learning auto-encoder network, including: applying a first weight value to a first statistical parameter value, wherein the first statistical parameter value comprises a variance value; and applying a second weight value to a second statistical parameter value, wherein the second statistical parameter value is not a variance value, wherein the first weight value is greater than the second weight value.
 19. The non-transitory computer-readable medium of claim 13, wherein the operations further comprise processing the plurality of input vectors with a multi-layered, unsupervised learning auto-encoder network, including: processing the plurality of input vectors with a first encoder having a first plurality of processing nodes and a first set of weights associated with the statistical parameter values to generate a first intermediate set of input vectors of smaller dimension than that of the plurality of input vectors; processing the first intermediate set of input vectors with a second encoder having a second plurality of processing nodes, which are fewer than the first plurality of processing nodes, and a second set of weights to generate a second intermediate set of input vectors of smaller dimension than that of the first intermediate set of input vectors; and processing the second intermediate set of input vectors with a third encoder having a third plurality of processing nodes, which are fewer than the second plurality of processing nodes, and a third set of weights to generate a third intermediate set of input vectors of smaller dimension than that of the second intermediate set of input vectors, wherein the LSTM layer of the neural network processes the third intermediate value.
 20. The non-transitory computer-readable medium of claim 13, wherein the statistical parameter values comprises one or more of, related to respective sets of discrete samples of the first data, a Fast Fourier Transform value, a maximum value, a minimum value, a mean value, a variance value, an entropy value, a mean cross rate value, a skewness value, or a kurtosis value. 